3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction

In the past, methods for hand sign recognition have been successfully tested in Human Robot Interaction (HRI) using traditional methodologies based on static image features and machine learning. However, the recognition of gestures in video sequences is a problem still open, because current detection methods achieve low scores when the background is undefined or in unstructured scenarios. Deep learning techniques are being applied to approach a solution for this problem in recent years. In this paper, we present a study in which we analyse the performance of a 3DCNN architecture for hand gesture recognition in an unstructured scenario. The system yields a score of 73% in both accuracy and F1. The aim of the work is the implementation of a system for commanding robots with gestures recorded by video in real scenarios.This work was funded by the Ministry of Economy, Industry and Competitiveness from the Spanish Government through the DPI2015-68087-R and the pre-doctoral grant BES-2016-078290, by the European Commission and FEDER funds through the project COMMANDIA (SOE2/P1/F0638), action supported by Interreg-V Sudoe